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Land surface temperature (LST) is a significant factor in surface energy balance and global climatology studies. Land cover (LC) and elevation are two factors that affect the change of LST, and their effects depend on different geography. This study aims to demonstrate an alternative approach to examine the change of LST during 20 years (2001 to 2020) on Taiwan Island and to investigate the effect of LC change and elevation on a decadal trend of LST using a linear model that adjusting for each determinate factor. MODIS LST and LC data, as well as GMTED2010 elevation product, were downloaded available website. The natural cubic spline function was used to model annual seasonal patterns in LST. Linear regression model was used to estimate decadal change of long-term LST time series. Weighted sum contrasts linear regression was used to assess the effect of LC transformation and elevation on the decadal LST change by comparing adjusting mean of all factors. The adopted analysis method was an appropriate approach to assess categorical factors than those based on treatment contrasts, requiring specifying a control group to compare means and confidence intervals. Results showed that there was an increase in LST for most of the island. The average daytime and nighttime LST trends were 0.12 and 0.31 °C/decade, respectively. However, areas in the southern part of the north–south direction mountain range show a statistically significant increase in LST in both daytime and nighttime. The major landslides caused this noticeable change of surface temperature due to the catastrophic damage of typhoon Morakot in 2009. The results also revealed that the different pattern of LC change has a significant effect on daytime LST, but not on nighttime LST trends. The elevation above 600 m had affected both daytime and nighttime LSTs. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) assessment can explain temperature variation, which may be influenced by factors such as elevation, land cover, and the normalized difference vegetation index (NDVI). In this study, a multiple linear regression model of LST variation was constructed based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra satellite, relating to the period, 2000-2018. The highest LST variation of nearly 1.3 °C/decade was found in savanna areas while the lowest variation was in the evergreen broadleaf forest and woody savanna, which experienced a decrease of 2.1 °C/decade. The overall mean change of LST was -0.4 °C/decade and the regression model with LST as the dependent variable and elevation, land cover type, and NVDI as independent variables produced an R square of0.376. The variation in LST was different depending upon the NDVI. [ABSTRACT FROM AUTHOR]